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Bowing detection for erhu learners using yolo deep learning techniques 22nd

Bowing detection for erhu learners using yolo deep learning ...

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22nd international conference on Human-Computer Interaction, HCII 2020

作者： Lu, Bonnie Dow, Chyi-Ren Peng, Chang-Jan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (纸本)9783030507282

The traditional ways to learn musical instruments are having lessons with teachers, or self-paced by teaching videos and books. Nowadays, the musical instrument self-learning systems are in the ascendant. However, it is difficult for a musical instrument learner to know if his playing posture is correct unless the teacher provides guidance in person and corrects it immediately. Due to the breakthrough development of computer vision technology, computers with camera lenses can act as teachers’ eyes to detect the learners’ movements and give some assistance for their self-learning. Erhu, a kind of Chinese traditional stringed instrument, is getting popular worldwide. In this research, taking the erhu bowing method as an example, we use YOLO object detection technology to track the trajectory of the bow in the video and evaluate whether it meets the level and straightness requirements. For the purpose, we first define the measurement methods of level and straightness and design a system to record the progress. In 2016, Joseph Redmon et al. proposed a real-time object detection method called YOLO, "You Only Look Once". The multi-layer CNN architecture is executed only once, which greatly improves the detection speed. To track the movement of erhu’s bow, we must at least detect the coordinates of two objects, the erhu bow and the right hand of the player who holding the bow. Our images data set to train the object detection model came from several Chinese music society in Taiwan. The experimental results show that the model can successfully mark the desired objects. Finally, the study found that even senior erhu players still have some deviations, but they are more stable than beginners. Self-correction through system prompts would help learners to reduce the bias problem. © Springer Nature Switzerland AG 2020.

关键词： Computer vision

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Fully CNN-based Pixel Level Classification model for Urban Land Image Acquired by Unmanned Aerial Vehicle 22

Fully CNN-based Pixel Level Classification model for Urban L...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Li, Yanqi Liu, Jianzhou (Tom) Zhi, Shaoming (Miles) Hamden Hall Country Day Sch 1108 Whitney Ave Hamden CT 06517 USA Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310000 Peoples R China

ISBN: (纸本)9781728116648

With the improvement of the spatial resolution of Unmanned Aerial Vehicle (UAV) imagery, the texture information of the surface structures becomes richer and more complex, which Making high-resolution image classification bring new opportunities and great challenges. Therefore, this paper proposes a novel imagery classification method based on U-Net fully convolution neural network combine with a conditional random field model to process this kind of images. First, deep and abstract visual features are extracted by convolution and pooling operations, and then feature map is reconstructed by deconvolution and upsampling operations. Then, a softmax classifier generates the initial classification results. Finally, conditional random field is used to optimize the classification results. The effectiveness and robustness of the proposed method is verified on the UAV imagery data collected from Guangdong Province. Experimental results indicate that the method proposed in this paper can effectively classify different objects with an overall accuracy larger than 91.09% and obtained an average 4.18% improvement over UAV imagery data.

关键词： UAV imagery classification Fully convolutional neural network City planning Deep learning Conditional random fields

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The impact of social and economic development on the spread of infectious respiratory diseases, push or constrain? Empirical research from China based on machine learning methods

The impact of social and economic development on the spread ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Ke Yuan Yabing Huang Qian Tang School of Computer and Information Engineering Henan University International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China College of Environment and Planning Henan University Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng China

ISBN: (纸本)9781728176505

It is unclear whether socioeconomic factors affect the incidence of respiratory infectious diseases in China. This article uses generalized linear model and generalized additive model to analyze the relationship between socioeconomic factors and infectious diseases. Studies have shown that both models can reflect the relationship between socioeconomic factors and infectious diseases, but generalized additive model has better goodness of fit. Nationwide, considering the impact of a single economic and social factor on the incidence of respiratory infectious diseases, the GDP Per capita, age structure of population, local fiscal budgetary receipts and proportion of higher education have a significant impact on the incidence rate of five diseases (p

关键词： Analytical models Additives Infectious diseases Pulmonary diseases Sociology Lung Machine learning

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V-PSC: a Perturbation-Based Causative Attack Against DL Classifiers' Supply Chain in VANET 22

V-PSC: a Perturbation-Based Causative Attack Against DL Clas...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Zeng, Yi Qiu, Meikang Niu, Jingqi Long, Yanxin Xiong, Jian Liu, Meiqin Xidian Univ Xian Peoples R China Harrisburg Univ Sci & Technol Dept Comp Sci Harrisburg PA 17101 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781728116648

DL Based classifiers can attain a higher accuracy with less storage requirement, which suits perfectly with the VANET. However, it has been proved that DL models suffer from crafted perturbation data, a small amount of such can misguide the classifier, thus backdoors can be created for malicious reasons. This paper studies such a causative attack in the VANET. We present a perturbation-based causative attack which targets at the supply chain of DL classifiers in the VANET. We first train a classifier using VANET simulated data which meets the standard accuracy for identifying malicious traffic in the VANET. Then, we elaborate on the effectiveness of our presented attack scheme on this pre-trained classifier. We also explore some feasible approaches to ease the outcome brought by our attack. Experimental results show that the scheme can cause the target DL model a 10.52% drop in accuracy.

关键词： VANET Perturbation Causative Attack Deep learning

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Federated learning Based Mobile Crowd Sensing with Unreliable User data

Federated Learning Based Mobile Crowd Sensing with Unreliabl...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Yuhong Jiang Rong Cong Chang Shu Anqi Yang Zhiwei Zhao Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China China University of Exeter UK

ISBN: (纸本)9781728176505

Mobile crowd sensing (MCS), as a novel paradigm that coordinates a crowd of distributed devices to complete a whole sensing task, has attracted tremendous attention. While providing an effective and practical approach for sensing in largescale mobile scenes, the existing works on MCS suffer from a risk of privacy leakage because user data needs to be gathered in the cloud for processing and analysis. Federated learning (FL) is a promising alternative as it can leverage mobile devices to accomplish a large learning task without centrally collecting the user data. However, incorporating FL into MCS is a non-trivial task due to the following reasons: 1) the data quality of mobile devices is often unreliable, especially in the context of crowd sensing; 2) the existing incentive mechanism in MCS may not work due to the lack of access to the user data. To address the problem, we propose a privacy-preserving mobile crowd sensing system based on Federated learning with unreliable user data (called F-Sense). We analyze the key issues of sensing tasks, and further design an incentive mechanism to reward and motivate participants. Moreover, we explore to construct a federated quality model of user data in order to improve the data quality and obtain better training results for sensing tasks. Extensive simulation results show that F-Sense achieves privacy-preserving crowd sensing and the developed incentive mechanism can improve the task efficiency by encouraging local training at mobile devices.

关键词： Training data privacy data integrity Computational modeling Simulation Collaborative work Mobile handsets

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An Order Dispatch System Based on Reinforcement learning for Ride Sharing Services

An Order Dispatch System Based on Reinforcement Learning for...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zeqiang Chen Peng Li Junlei Xiao Lei Nie Yu Liu College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9781728176499;9781728176505

Ride-sharing has been widely used in many cities, such as Didi and Uber. Ride-sharing is regarded as an effective way to solve urban traffic congestion and pollution. However, most of the existing dispatch methods take the minimization of the travel distance as the optimization goal, without considering other factors. In this paper, we consider not only detour distance, but also consider seat utilization, future profit, and hidden profit. Firstly, we propose a deep evaluation network to evaluate factors that affect vehicle dispatch, and we exploit the reinforcement learning strategy to train the deep evaluation network. Then, we propose the dynamic external factor calculation (DEFC) algorithm to calculate those factors. Secondly, to calculate the driver's future profit, we propose a prediction model based on the K-Nearest Neighbor(KNN) to predict the number of passengers and vehicles. Thirdly, we use a vehicle search method to search vehicles that satisfy passenger spatial-temporal constraint. Based on a real-world dataset in Rome, we evaluate our algorithm to confirm the effectiveness of our method.

关键词： Search methods Heuristic algorithms Urban areas Reinforcement learning Predictive models Prediction algorithms Dispatching

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Iterative Bayesian learning for Crowdsourced Regression 22

Iterative Bayesian Learning for Crowdsourced Regression

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22nd international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Ok, Jungseul Oh, Sewoong Jang, Yunhun Shin, Jinwoo Yi, Yung Univ Washington Seattle WA 98195 USA Korea Adv Inst Sci & Technol Daejeon South Korea

Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme.

关键词： Mean square error

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Features of development and use in the educational process of nrnu mephi tutoring integrated expert ystems in the field of study «software engineering» 22

Features of development and use in the educational process o...

引用

22nd international conference "Enterprise engineering and Knowledge Management", EEKM 2019

作者： Rybina, Galina V. Nikiforov, Andrey Y. Fontalina, Elena S. Sorokin, Ilya A. National Research Nuclear University MEPhI Moscow Russia

The purpose of this work is to show some methodological aspects of software engineering, reflecting the departmental view that has developed over the years on issues of training specialists in the field of PT, and also highlight and briefly characterize modern innovations that are currently used for practical implementation of the educational process on the basis of the ontological approach and the use of tutoring integrated expert systems (IES) to support basic courses / disciplines. The formation of the modern scientific and educational appearance of the department in the field of PT is naturally associated with the historical, conceptual and technological integration of three specializations ("Computer Software", "intelligent Systems and Technologies" and "automated Information Systems") within the "Software engineering" training area, which is reflected in the creation of a single ontological space of knowledge and skills on the basis of which, using the technology of teaching IES, competence-oriented models of graduates were constructed. The ontological approach integrates heterogeneous knowledge, data, documents (FSES standards) and others allows you to effectively organize the educational process in the field of "Software engineering" using the most modern technologies in the development and use of intelligent systems of various classes. © 2019 CEUR-WS. All rights reserved.

关键词： intelligent systems

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Bangla intelligence question answering system based on mathematics and statistics 22

Bangla intelligence question answering system based on mathe...

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22nd international conference on Computer and Information Technology, ICCIT 2019

作者： Kowsher, Md. Rahman, M.M. Mahabubur Ahmed, S.K. Shohorab Prottasha, Nusrat Jahan Noakhali Science and Technology University Dept. of Applied Mathematics Noakhali3814 Bangladesh Noakhali Science and Technology University Dept. of Cste Noakhali3814 Bangladesh University of Rajshai Dept. of Information and Communication Engineering Rajshai6205 Bangladesh Daffodil International University Dept of CSE Dhaka Bangladesh

ISBN: (纸本)9781728158426

The Bangla Informative Question Answering System (BIQAS) is a significant Machine learning (ML) technique that helps a user to trace relevant information by Bengali Natural Language Processing (BNLP). In this research paper, we have applied three mathematical and statistical procedures for BIQAS based on question answering data. These procedures are cosine similarity, Jaccard similarity, and Naive Bayes algorithm. The cosine similarity has interacted with dimension reduction technique SVD on user questions and questions answering data in order to reduce the space and time complexity. These procedures of this research are separated into two parts: pre-processing data and establishment of a relationship between user's questions and contained informative questions. We have got 93.22% accurate answer by using cosine similarity, 84.64% by Jaccard similarity and 91.31% by Naïve Bayes algorithm. © 2019 IEEE.

关键词： Machine learning

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Edge Intelligence Empowered Distribution Path Planning with Internet of Vehicles

Edge Intelligence Empowered Distribution Path Planning with ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Tiancai Li Yiping Wen Zheng Tan Hong Chen School of Computer Science and Engineering Hunan University of Science and Technology China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology

ISBN: (纸本)9781728176505

With rapid urbanization and increasing traffic, distribution path planning has become an important challenging issue. Traditional vehicle distribution route planning mostly relies on static road information or historical traffic data for route planning. However, route planning based on such information may be unreasonable, because many uncertainty factors involve in the vehicle distribution process. Edge intelligence can help to handle such uncertainty factors, as it can offer smart services in close proximity to the Internet of Vehicles (IoV) environment with low latency and less cost. Therefore, in this paper, a model for dynamic distribution path planning with IoV and edge intelligence is constructed, and a corresponding planning strategy is proposed. The result of simulation experiments shows its effectiveness.

关键词： Uncertainty intelligent vehicles Computational modeling Roads High performance computing Path planning Planning

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